from __future__ import annotations

from typing import TypedDict
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     42
							UpperCAmelCase				   =     42




def 					_snake_case	(  __snake_case						):
					if not isinstance(__snake_case  ,     __snake_case						):
										raise TypeError('''The parameter s type must be str.'''						)

					return [s[i:] + s[:i] for i in range(len(__snake_case						)						)]
def 					_snake_case	(  __snake_case						):
					if not isinstance(__snake_case  ,     __snake_case						):
										raise TypeError('''The parameter s type must be str.'''						)
					if not s:
										raise ValueError('''The parameter s must not be empty.'''						)

					_UpperCamelCase			     =     all_rotations(__snake_case						)
					rotations.sort()  # sort the list of rotations in alphabetically order
					# make a string composed of the last char of each rotation
					_UpperCamelCase			     =     {
					    "bwt_string": "".join([word[-1] for word in rotations]						),
					    "idx_original_string": rotations.index(__snake_case						),
					}
					return response
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					if not isinstance(__snake_case  ,     __snake_case						):
										raise TypeError('''The parameter bwt_string type must be str.'''						)
					if not bwt_string:
										raise ValueError('''The parameter bwt_string must not be empty.'''						)
					try:
										_UpperCamelCase			     =     int(__snake_case						)
					except ValueError:
										raise TypeError(
										    '''The parameter idx_original_string type must be int or passive'''
										    ''' of cast to int.'''						)
					if idx_original_string < 0:
										raise ValueError('''The parameter idx_original_string must not be lower than 0.'''						)
					if idx_original_string >= len(__snake_case						):
										raise ValueError(
										    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''						)

					_UpperCamelCase			     =     [''''''] * len(__snake_case						)
					for _ in range(len(__snake_case						)						):
										for i in range(len(__snake_case						)						):
															_UpperCamelCase			     =     bwt_string[i] + ordered_rotations[i]
										ordered_rotations.sort()
					return ordered_rotations[idx_original_string]


if __name__ == "__main__":
					_lowerCAmelCase       =		"Provide a string that I will generate its BWT transform: "
					_lowerCAmelCase       =		input(entry_msg).strip()
					_lowerCAmelCase       =		bwt_transform(s)
					print(
					    f'Burrows Wheeler transform for string \'{s}\' results '
					    f'in \'{result["bwt_string"]}\''
					)
					_lowerCAmelCase       =		reverse_bwt(result["bwt_string"], result["idx_original_string"])
					print(
					    f'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
					    f'we get original string \'{original_string}\''
					)







"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






